E-triage: an electronic emergency triage system

ABSTRACT

An embodiment in accordance with the present invention provides a system that uses simple standardized patient information routinely collected at triage to distribute patients amongst triage levels based on critical and time-sensitive outcomes. The present invention estimates the probability of electronic medical record (EMR) recorded events for patients at triage. Predictions are made for patients based upon clinical information routinely collected at triage which include demographics (age and gender), vital signs (temperature, heart rate, systolic blood pressure, respiratory rate, and oxygen saturation), complaint(s), medical/surgical history, chronic conditions, and mode of arrival. Vital signs are categorized as normal or gradations of abnormal.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 62/296,753 filed Feb. 18, 2016, which is incorporated byreference herein, in its entirety.

GOVERNMENT SPONSORSHIP

This invention was made with government support under Agency forHealthcare Research in Quality (AHRQ) R21 HS23641-01A1 and2010-ST-061-PA0001 awarded by the Department of Homeland Security andthe National Center for the Study of Preparedness and Catastrophic EvenResponse. The government has certain rights in the invention.

FIELD OF THE INVENTION

The present invention relates generally to hospital resource managementand patient safety. More particularly, the present invention relates toemergency department management software to support objective,risk-based triage evaluation.

BACKGROUND OF THE INVENTION

Increasing patient visits and decreasing capacity due to closingfacilities has driven crowded emergency departments (ED) to a breakingpoint. As ED patient volumes rise, without a corresponding increase inresources, an increasing number of patients must wait to receive medicalevaluation and treatment. Thus, to effectively manage large volumes ofpatients, ED staff must quickly distinguish patients with critical andtime-sensitive conditions from those with less urgent needs. Thoughpatients at the extremes of acuity may be easy to identify, the clinicalcourses of the majority of patients are not obvious. Under-triagedpatients potentially suffer otherwise avoidable deterioration,morbidity, and mortality. Over-triaged patients consume limitedresources that may be of greater benefit to those with a higher acuityillness. Given the heightened levels of demand for ED services, anaccurate triage system is required to provide safe and optimal patientcare.

Further, the growing numbers of patients using the ED for non-emergencycare has spurred EDs to use triage to stream patients to specificservice lines (often separate care areas) based on acute risk andprojected resource needs. For example, an increasing number of EDsemploy “fast-tracks” to provide non-urgent care for patients to receivelimited evaluation and treatment. Safely identifying these patients atpresentation is needed to decrease low-acuity patient waiting andprevent ED resource over-utilization, which plagues this costly caresetting. To place in context, it has been estimated that between 13% and56% of ED visits are for non-urgent conditions representing between $24and $38 billion in wasteful healthcare spending annually.

Although triage has been a long-standing principle in emergencymedicine, standardized triage tools are relatively new. Countries suchas Canada, Australia, and the United Kingdom have created their owntriage instruments; similarly, in the United States, 72% of ED patientvisits are assessed using the Emergency Severity Index (ESI). The ESI iscomposed of a series of 3 questions used to assign patients to one of 5acuity levels. The ESI triage process relies on experienced nursejudgment to assess patients according to the following questions: (1) Isthe patient dying?; (2) Should the patient wait?; and (3) How manyresources will this patient require?

Patients dying are categorized to Level 1 (immediate treatment);patients who should not wait are categorized to Level 2 (emergenttreatment); patients deemed safe to wait are stratified to levels 3(urgent treatment) through 5 (non-urgent treatment) by anticipatedresource utilization with Level 3 requiring the most resources and Level5 the least. Thus, the ESI tool stratifies patients based upon nurses'experience and “sixth sense” for immediacy of medical need and resourceutilization. Including resource utilization in the determination oftriage level, makes the system unique among modern triage systems.

Though currently in widespread use across the US, ESI has severalshortcomings. First, ESI has not been adequately validated againstoutcomes indicating time sensitive or critical care needs. Further, itdoes not sufficiently distribute patients across the 5 triage levels,resulting in poor discrimination. Almost half of all ED patientsnationally are categorized as Level 3, the ambiguous andundifferentiated midpoint of a 5-level system. This results in patientsof a wide range of severity clustered in one large group, potentiallydelaying care to those most ill, countering the objective of triage.Finally, ESI relies heavily on subjective classification and maytherefore be limited by inherent variability, inexperience or humanerror with potential for misclassification.

The electronic triage system reduces this subjectivity, and wouldimprove triage reliability while maintaining validity and ease of use.Therefore, it would be advantageous to provide an automated electronictriage system, based on patient outcomes with improved discrimination.

SUMMARY OF THE INVENTION

The foregoing needs are met, to a great extent, by the presentinvention, wherein in one aspect a method for emergency department (ED)triage includes assessing individual patients that have presented to theED, but are waiting for care. The method includes gathering routinelycollected demographic and clinical data computing individual riskassessments. Risk estimates are computed for outcomes that include: (1)in-hospital (including ED) mortality, (2) intensive care unit admission,(3) emergent surgical procedure including catheterization, and (4)inpatient hospitalization, (5) other acute clinical outcomes availablein electronic medical record.

Additionally, the method includes using this risk assessment todetermine a broad care pathway (i.e., stream) in the ED. In accordancewith an aspect of the present invention, the method includes using anon-transitory computer readable medium for the execution of steps ofthe method. The method includes gathering data related to the potentialpatients comprising age, gender, mode of arrival, blood pressure, heartrate, temperature, respiratory rate, oxygen saturation, complaints,medical/surgical history and chronic conditions. All of these predictordata (or a sub-set) is used to estimate risk for each outcome. Themethod includes assigning each one of the patients a triage score on acustomizable scale (e.g., 1 to 5) based on risk estimates computed forthe patient.

In accordance with another aspect of the present invention, the methodincludes displaying information related to the patient plan. A specificpatient score and patient scores for the ED can also be displayed. Themethod includes alerting the user based on patient risk. Further, themethod can include prompting a health care provider for additionalinformation on the patient.

In accordance with still another aspect of the present invention, asystem for emergency department (ED) triage a non-transitory computerreadable medium programmed for gathering data for patients waiting to betriaged in the ED. The system includes assessing risks for each one ofthe potential patients waiting in the ED. The system also includesdetermining a patient plan based on the risk for each one of thepotential patients waiting in the ED.

In accordance with another aspect of the present invention, the systemincludes a computing device selected from a group of a personalcomputer, laptop, smartphone, tablet, server, and cloud based computingdevice. Gathering data related to the potential patients includesgathering any of a group of demographics (age and gender), vital signs(temperature, heart rate, systolic blood pressure, respiratory rate, andoxygen saturation), complaint(s), medical/surgical history, chronicconditions, and mode of arrival. The system includes assigning each oneof the patients a score indicating the risk to the patient andpredicting a patient's needs in the ED at triage. A patient's needs inthe ED at triage are predicted using data selected from a group of (1)in-hospital (including ED) mortality, (2) intensive care unit admission,(3) emergent surgical procedure including catheterization, and (4)inpatient hospitalization, (5) other acute clinical outcomes availablein electronic medical record. The system includes a display. The displaycan show a specific patient score as well as a heatmap of patient scoresfor the ED. The system can be used for alerting the user based onpatient risk and prompting a health care provider for additionalinformation on the patient.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings provide visual representations, which will beused to more fully describe the representative embodiments disclosedherein and can be used by those skilled in the art to better understandthem and their inherent advantages. In these drawings, like referencenumerals identify corresponding elements and:

FIG. 1 illustrates e-triage predictors and algorithm with an exampleemergency department risk profile. The risk profile translates predictedprobabilities of outcomes to e-triage levels. Example outcomes arecritical care (in-hospital mortality or ICU admission), emergentprocedure, and hospital inpatient admission.

FIG. 2 illustrates graphical views of characteristics of e-triagecompared to ESI for e-triage risk profiles derived to distributepatients across triage levels identical to ESI. Characteristics includethe proportion with critical care and emergent procedure outcomes(Column A), proportion admitted (B) proportion with elevated troponins(C), and proportion with elevated lactates (D) stratified by triagelevel for three independent ED populations: (1) a nationallyrepresentative sample collected by the Center for Disease Control (CDC)between 2008 and 2011, and a cohort from an (2) urban and (3) communityED between Aug. 1, 2014 and Oct. 1, 2015.

FIG. 3 illustrates an exemplary E-triage Epic Interface.

DETAILED DESCRIPTION

The presently disclosed subject matter now will be described more fullyhereinafter with reference to the accompanying Drawings, in which some,but not all embodiments of the inventions are shown. Like numbers referto like elements throughout. The presently disclosed subject matter maybe embodied in many different forms and should not be construed aslimited to the embodiments set forth herein; rather, these embodimentsare provided so that this disclosure will satisfy applicable legalrequirements. Indeed, many modifications and other embodiments of thepresently disclosed subject matter set forth herein will come to mind toone skilled in the art to which the presently disclosed subject matterpertains having the benefit of the teachings presented in the foregoingdescriptions and the associated Drawings. Therefore, it is to beunderstood that the presently disclosed subject matter is not to belimited to the specific embodiments disclosed and that modifications andother embodiments are intended to be included within the scope of theappended claims.

An embodiment in accordance with the present invention provides a systemthat uses simple standardized patient information routinely collected attriage to distribute patients amongst triage levels based on paralleloutcomes in tandem. The present invention estimates the probability ofone or any subset of: (1) in-hospital (including ED) mortality, (2)intensive care unit admission, (3) emergent surgical procedure includingcatheterization, and (4) inpatient hospitalization, (5) other acuteclinical outcomes available in electronic medical record. Risk for eachoutcome may be estimated in some composite form or individually.In-hospital mortality is defined as death in the ED or in-hospitalduring the index visit. “Emergent” for emergent procedure is measuredwithin some time within 72 hours of ED disposition. The hospitalizationoutcome is defined as any admission to an inpatient care sites includingward, intensive care, or direct transfer to an external acute carehospital. Patients transitioned to observation status/care areas werenot considered an admission unless their observation ultimately resultedin inpatient hospitalization.

Probabilistic predictions of these outcomes are made for patients basedupon clinical information routinely collected at triage which includebasic demographics (age and gender), vital signs (temperature, heartrate, systolic blood pressure, respiratory rate, and oxygen saturation),complaint(s), medical/surgical history, chronic conditions, and mode ofarrival. All of these predictor data (or a sub-set) is used to estimaterisk. Vital signs are categorized as normal or gradations of abnormal asseen in FIG. 1. Medical ontologies derived from clinician consensus(modified-Delphi technique) and data from multiple sites have been usedto categorize text-based complaints, history, and chronic conditionsinto clinically meaningful categories. An example of categorization ofchief complaints for this may be seen in Table 1 below. Featureselection methods may be applied (or not) to optimize the groupingstructure of these text-based predictors to maximize predictiveperformance with respect to our two outcomes.

TABLE 1 Example map of clinical groupings for EMR-entered chiefcomplaints Clinical Groupings Electronic Medical Record EnteredComplaints Abdominal pain Abdominal pain; Abdominal cramping; Colitis;Constipation; Cramps; Gastroesophageal Reflux; Flank Pain; GastroparesisChest pain Chest pain; Chest tightness; Chest burning; Chest discomfort;Lung pain; Heart problem; Shortness of breath Shortness of breath;Airway obstruction; Aspiration; Breathing problem; Hyperventilation;Hypoxia; Respiratory Distress

The present invention, referred to in its various embodiments asE-Triage, herein, was derived using a supervised machine learningprediction engine (FIG. 1) trained from a retrospective cohort of226,317 ED visits for adults from three separate populations: (1) anationally representative sample, (2) an urban ED (Johns HopkinsHospital, Baltimore, Md.), and (3) a community ED (Howard County GeneralHospital, Columbia, Md.). ED visit data for 53,591 encounters wascollected through the nationally representative National HospitalAmbulatory Care Survey (NHAMCS) conducted by the Center for DiseaseControl (CDC) between 2008 and 2011. This multi-year sample wascollected from between 357 and 373 reporting hospitals each yearrandomly sampled from a set of 600 eligible hospitals. Patient encounterdata for 60,712 and 112,014 visits from the urban and community EDs,respectively, was collected over one year between 8-1-2014 and 10-1-2015from the electronic medical record (EMR) system at each hospital.Characteristics of these populations may be seen in Table 2.

TABLE 2 Emergency department patient visit outcomes and characteristicsNational Population Urban ED Community ED Sample Cohort Size (N) 60,712112,014 53,591 Predicted Outcomes (%) Critical Care Outcome 2.0 1.6 2.3In-Hospital Mortality 0.3 0.5 0.4 ICU Admission 1.8 1.3 2.0 EmergentSurgery Outcome 1.4 1.7 0.9 Catheterization 0.1 0.2 0.2 Hospitalization(Inpatient) Outcome 26.0 22.3 15.9 Secondary Outcomes (%) ElevatedTroponin 2.5 1.8 — Elevated Lactate 3.6 1.2 — Demographics and ArrivalMode Age: Mean (95% Percentile) 44 (29-57) 49 (33-66) 43 (29-59) Gender:Female (%) 52.7 57.9 57.4 Arrival by Ambulance (%) 20.4 25.5 18.6 VitalSigns (% Low | % Normal | % High) Temperature 0.3 | 97.3 | 2.4 0.2 |97.9 | 1.9 0.3 | 97.5 | 2.2 Heart Rate 0.3 | 89.1 | 10.5 0.5 | 90.5 |9.0 0.4 | 90.4 | 9.3 Respiratory Rate 0.4 | 97.0 | 2.5 1.2 | 96.0 | 2.91.3 | 92.8 | 6.0 Systolic Blood Pressure 2.4 | 95.2 | 2.4 3.7 | 95.3 |1.0 3.0 | 95.5 | 1.5 Oxygen Saturation 3.2 | 96.8 | — 5.9 | 94.1 | — 6.6| 93.4 | — Chief Complaint Sample (%) Abdominal Pain 11.6 14.6 11.0Chest Pain 7.8 9.3 7.3 Shortness of Breath 5.9 5.8 5.1 Back Pain 3.1 3.35.0 Headache 3.1 2.8 4.0 Active Chronic Problems Sample (%) Diabetes 8.43.6 Coronary Artery Disease (CAD) 4.0 2.0 — Congestive Heart Failure(CHF) 3.3 1.8 — Atrial Fibrillation (AFib) 2.3 2.1 — End Stage RenalDisease (ESRD) 1.7 0.8 —

E-triage uses routine information collected at triage to distributeadult patients across a customizable scale based on risk of pre-definedEMR collected outcomes. Ensemble learning is used to create randomforest decision tree models for each outcome, per population. Thus,separate models are derived for each outcome from the same predictordata, but are applied in tandem to produce probabilities that map toe-triage levels as seen in FIG. 1. For example in FIG. 1, high severitypatients with a probability of critical care (ICU or in-hospitalmortality)≧15% or emergent procedure≧15% were assigned to e-triage Level1 and probabilities between 5% and 15% were assigned to e-triage level2. Patients with probabilities of critical care between 2% and 5% oremergent procedure between 2% or 5% or hospitalization>10% were assignedLevel 3. The remaining patients with<10% probability of hospitalizationwere then stratified by their probability of admission (i.e., resourceintensity surrogate). It is important to note that risk profile (i.e.,cut-off thresholds) that define patients grouped to Level 1 through 5are not static and may be adapted (i.e., customized) to individual EDsobjectives for risk stratification, distribution of patients, andresource allocation.

E-triage demonstrated an out-of-sample AUC ranging from 0.86 to 0.92 forthe critical care outcome, 0.73 to 0.82 for the emergent procedureoutcome, and 0.82 to 0.84 for the hospitalization outcome across EDpopulations. FIG. 2 illustrates graphical views of characteristics ofe-triage compared to ESI on for the three ED populations. FIG. 2 fromleft-to-right exhibits the proportion of ED visits with predicted andsecondary outcomes stratified by triage level. E-triage was able toidentify an equal or greater proportion of patients with critical careand emergent procedure outcomes (column A) to Level 1 than the ESIreference standard. The proportion of ED visits with a critical care oremergent procedure outcome in Level 1 patients was: 22.9% e-triageversus 16.9% ESI (difference 6.0%, 95% CI 3.0%, 8.9%) in the urban ED;49.1% e-triage versus 50.5% ESI (difference −1.4%, CI −6.9%, 4.2%) inthe community ED, and; 26.4% e-triage versus 14.7% ESI (difference11.7%, 95% CI 8.4%, 14.9%) in the national sample. E-triage detectionwas slightly, but not meaningfully, decreased for the community ED whereless than 1% of the total population is triaged to Level 1. Comparativeresults are displayed for the hospitalization outcome (FIG. 3; columnB). E-triage's ability to detect secondary clinical outcomes wassimilarly equal or greater the ESI reference standard (FIG. 3; column Cand D) at both study sites. The most substantial difference was forLevel 1 patients in the urban ED where overall prevalence of elevatedtroponin (>0.6 ng/ML) and lactate (>2.4 mmol/L) was higher than thecommunity ED.

Significant reclassification of patient visits occurred (e.g., patientwith ESI Level 3 and e-triage Level 2) despite evaluating e-triage tomatch the distribution of patients across triage levels observed by ESI.Much of this reclassification was exhibited in the large majority of ESILevel 3 patients as seen in Table 2. The rate of agreement for ESI Level3 patients was lower for the national sample (56.2%) compared to theurban (76.6%), and community (74.7%) EDs. Substantial differences inoutcome rates between patients in agreement, under-triaged, orover-triaged highlight the opportunity for improved outcomes-basedpatient differentiation. Compared to patients where Level 3 was inagreement for e-triage and ESI, under-triaged patients where at least 5times more likely to experience the critical care outcome or emergentsurgery outcome, 2 times likely to be admitted to the hospital, and over2 times more likely to yield elevated troponin or lactate (Table 3)levels. A more substantial inverse trend may be seen for thoseover-triaged patients.

TABLE 3 Outcomes for reclassified Emergency Severity Index Level 3patients (majority group). Predicted Outcomes Secondary Outcomes Re-Critical Emergent Hospital- Elevated Elevated Population class N CareProcedure ization Troponin Lactate Urban Agree¹ 31,456 0.7% 1.2% 24.6%2.0% 3.0% ED (76.6%) Under- 4,823 3.5% 3.0% 47.7% 5.6% 6.6% Triage²(11.7%) Over- 4,768 0.0% 0.8%  5.3% 0.0% 0.3% Triage³ (11.6%) CommunityAgree¹ 55,383 0.3% 1.7% 19.4% 1.0% 0.4% ED (74.7%) Under- 9,503 2.2%4.2% 44.3% 3.6% 2.1% Triage² (12.8%) Over- 9,224 0.0% 0.2%  3.8% 0.1%0.0% Triage³ (12.5%) National Agree¹ 14,443 1.4% 1.3% 17.6% — — Sample(56.2%) Under- 4,150 7.1% 2.1% 45.2% — — Triage² (16.1%) Over- 7,1230.2% 0.2%  3.7% — — Triage³ (27.7%) Definitions: ¹Agree occurred when apatient's e-triage and ESI levels were equivalent ²Under-triage occurredwhen e-triage estimated a patient higher-risk than ESI (i.e., e-triage <ESI) ³Over-triage occurred when e-triaged estimated a patient to belower risk than ESI (i.e., e-triage > ESI)

Development of the e-triage application will benefit from knowledgegained by prior published efforts to create and apply clinical decisionsupport. In fact, a systematic review of 70 clinical decision supportsystems in practice found 4 critical features (amongst 15 evaluated)most associated with likelihood of improving clinical care: 1. Provisionof decision support as part of workflow. The e-triage applicationperforms triage which is a required initial step for all patients thatenter the ED. E-triage relies on the exact same data entry thatcurrently exists in most EDs across the US making it a natural fit intocurrent workflow. 2. Provision of recommendations rather thanassessments. E-triage produces a triage level that is directlyactionable. Triage levels are used to prioritize patients and in manyEDs (including Johns Hopkins) direct patients down pathways whereresource intensity and care processes differ. Provision of decisionsupport at the time and location of decision making. The E-triage Levelis issued immediately at triage and is required to direct future care.4. Uses a computer to generate decision support. E-triage uses IT toapply it's algorithm to patients and display results. E-triage meetsthese criteria for successful clinical decision support and provider-ITinteractions at triage. Technical details of software and interfacedevelopment follow.

The application (interface, algorithm engine, and database) has beendeployed in two separate ways: (1) embedded into existing HospitalInformation Systems (Epic) and (2) as a free-standing, public-facing webapplication. Programming for each deployment strategy requires the samefoundation and will overlap substantially. However, the embeddedapplication must be integrated into Epic. At triage, routine clinicalinformation is entered into Epic's ED Information System (ASAP). Nextthe user views (i.e., one-click) the E-triage application interface forthat specific patient, as illustrated in FIG. 3. This process willensure no duplicate data entry and seamless integration into workflow.In addition, the application will store triage process time-stamps tosupport evaluation. This specifically includes the time when ASAP wasopened for a patient at triage (triage start) and the time a finalE-triage Level is issued (triage end). Alternatively, the free-standingweb application will not require interoperability.

The E-triage interface will evolve and has been designed by triage nurseusers. The current Epic-embedded version is in FIG. 3. Data entered intoEpic at triage will be automatically communicated to the E-triageapplication. These fields and layout mimic the currently existing triageinterface. It's important to note the 3 questions below the “ElectronicTriage Level Recommendation” highlight requirements to maintain somenurse judgment by including an override feature. The frequency, type(i.e., under-triage or over-triage) and reasons for override will becollected by the application and explicitly analyzed to improve E-triageiteratively. A display according to the present invention shows a triagelevel for a patient in question. However, it can also transform thetriage level data for a number of patients waiting in the ED into avisual representation of patients and priority, for example as a heatmapof patients waiting for care. The present invention can also displayalerts regarding specific patients or for the ED as a whole. Thesealerts need not be prescheduled and can be disseminated over a widerange of devices.

Other interface features of note include drop-down menus for chiefcomplaint and medical history fields (i.e., not free text), form-logicfor demographic and vital sign fields, and a population selectionfunction (top right corner) available only in the web-based version.Web-based users outside of Johns Hopkins will be able to select “Urban”,“Community” or “National” corresponding to the E-triage algorithmdeveloped for each population, respectively. This will allow outsideusers to explore differences in these populations or select thepopulation that is most relevant to their application.

These steps can be carried out using a non-transitory computer readablemedium loaded onto a computing device such as a personal computer,tablet, phablet, smartphone, computer server, cloud based computingdevice, or any other computing device known to or conceivable by one ofskill in the art. Indeed, any suitable hardware and software known to orconceivable by one of skill in the art could be used. It should also benoted that while specific equations are detailed herein, variations onthese equations can also be derived, and this application includes anysuch equation known to or conceivable by one of skill in the art.

A non-transitory computer readable medium is understood to mean anyarticle of manufacture that can be read by a computer. Suchnon-transitory computer readable media includes, but is not limited to,magnetic media, such as a floppy disk, flexible disk, hard disk,reel-to-reel tape, cartridge tape, cassette tape or cards, optical mediasuch as CD-ROM, writable compact disc, magneto-optical media in disc,tape or card form, and paper media, such as punched cards and papertape. The computing device can be a special computer designedspecifically for this purpose. The computing device can be unique to thepresent invention and designed specifically to carry out the method ofthe present invention.

The computing device should include a display and a user input. Thissetup allows for the user to change parameters for location, department,patient criteria, etc. This setup also allows for the update of thevisual feedback as parameters are changed. It is also possible that thedisplay shows predictions of various ED capacity outcomes. The computingdevice can also be networked with or otherwise in communication withelectrical medical records systems and software. In some embodiments thecomputing device can be configured to communicate directly via wire orwirelessly with hospital computers, databases, or other systems.

The many features and advantages of the invention are apparent from thedetailed specification, and thus, it is intended by the appended claimsto cover all such features and advantages of the invention which fallwithin the true spirit and scope of the invention. Further, becausenumerous modifications and variations will readily occur to thoseskilled in the art, it is not desired to limit the invention to theexact construction and operation illustrated and described, andaccordingly, all suitable modifications and equivalents may be resortedto, falling within the scope of the invention.

What is claimed is:
 1. A method for emergency department (ED) triagecomprising: gathering data for patients waiting to be triaged in the ED;assessing risks for each one of the potential patients waiting in theED; determining a patient plan based on the risk for each one of thepotential patients waiting in the ED.
 2. The method of claim 1 furthercomprising using a non-transitory computer readable medium for theexecution of steps of the method.
 3. The method of claim 1 whereingathering data related to the potential patients comprises gathering anyof a group consisting of demographics (age and gender), vital signs(temperature, heart rate, systolic blood pressure, respiratory rate, andoxygen saturation), complaint(s), medical/surgical history, chronicconditions, and mode of arrival.
 4. The method of claim 1 furthercomprising assigning each one of the patients a score indicating therisk to the patient
 5. The method of claim 1 further comprisingpredicting a patient's needs in the ED at triage.
 6. The method of claim5 further comprising predicting a patient's needs in the ED at triageusing data selected from a group consisting of (1) in-hospital(including ED) mortality, (2) intensive care unit admission, (3)emergent surgical procedure including catheterization, and (4) inpatienthospitalization, (5) other acute clinical outcomes available inelectronic medical record.
 7. The method of claim 1 further comprisingdisplaying information related to the patient plan.
 8. The method ofclaim 4 further comprising displaying one selected from a groupconsisting displaying a specific patient score and patient scores forthe ED.
 9. The method of claim 1 further comprising alerting the userbased on patient risk.
 10. The method of claim 1 further comprisingprompting a health care provider for additional information on thepatient.
 11. A system for emergency department (ED) triage comprising: anon-transitory computer readable medium programmed for: gathering datafor patients waiting to be triaged in the ED; assessing risks for eachone of the potential patients waiting in the ED; determining a patientplan based on the risk for each one of the potential patients waiting inthe ED.
 12. The system of claim 11 further comprising a computing deviceselected from a group consisting of a personal computer, laptop,smartphone, tablet, server, and cloud based computing device.
 13. Thesystem of claim 11 wherein gathering data related to the potentialpatients comprises gathering any of a group consisting of demographics(age and gender), vital signs (temperature, heart rate, systolic bloodpressure, respiratory rate, and oxygen saturation), complaint(s),medical/surgical history, chronic conditions, and mode of arrival. 14.The system of claim 11 further comprising assigning each one of thepatients a score indicating the risk to the patient
 15. The system ofclaim 11 further comprising predicting a patient's needs in the ED attriage.
 16. The system of claim 15 further comprising predicting apatient's needs in the ED at triage using data selected from a groupconsisting of (1) in-hospital (including ED) mortality, (2) intensivecare unit admission, (3) emergent surgical procedure includingcatheterization, and (4) inpatient hospitalization, (5) other acuteclinical outcomes available in electronic medical record.
 17. The systemof claim 11 further comprising a display.
 18. The system of claim 14further comprising displaying one selected from a group consistingdisplaying a specific patient score and patient scores for the ED. 19.The system of claim 11 further comprising alerting the user based onpatient risk.
 20. The system of claim 11 further comprising prompting ahealth care provider for additional information on the patient.